全方位カメラを用いたテンプレートマッチングに基づくモンテカルロ位置同定法

Abstract

This paper presents a robust self-localization method based on template matching and Monte Carlo Localization in the RoboCup Middle-Size League. The method of this paper can be self-localized by template matching using information of field lines and a center circle instead of colored landmarks such as goals and corner poles. In RoboCup, real-time processing is crucial. The method can reduce computational costs of template matching using dynamically generated templates. The templates of the field are generated and matched from information of white lines and an electronic compass sensor in real-time. Moreover, the method can self-localize directly from the template matching. Thus, the kidnapped robot problem is not occurred. The experimental results indicate that the proposed method can self-localize robustly and in real-time under even partial occlusion. This method is suitable for the RoboCup Middle-Size League.